In a computing environment, system monitoring, establishing correlations and organizing systems constitutes an important task to improve overall performance of the system in information technology (or data processing) environment. In general, the system comprises of a plurality of hardware and software components. As the systems grow large and complex, the interactions between the hardware and software components become hard to maintain and manage or even trust. Any failure in the system may cause the system to freeze, reboot, or stop functioning altogether that may lead to data loss.
Existing systems do monitoring or correlation or rule based analysis of the data, but in isolation. Further, still many applications use manually set thresholds to discover Service Level Agreement (SLA) violations or to approach violations and to issue alters regularly. However, none of these systems simultaneously use a rule based analysis, correlation and statistical analysis to predict possible system failures, SLA and threshold violations.
Predictive analysis identifies risks and failures by statistical analysis by extracting present and historical information from data and using it to predict future trends and behavior patterns. The analysis is vital for system monitoring purposes.
Some of the tools known in prior art are: IBM Tivoli Monitoring software that helps in optimizing IT infrastructure performance and availability beside detecting and recovering potential problems in essential system resources automatically. PRTG is a network-monitoring tool that ensures smooth running of computer systems are running smoothly with minimal or no outrages at all.
Various built-in command and a few add-on tools are known in prior art equipped with tons of monitoring. These tools provide metrics, which can be used to get information about system activities. These tools can be used to find the possible causes of a performance problem.
However, the above-discussed tools exist in their own silos and are not associated in a meaningful and usable manner. None of these systems uses the behavior of the current IT environment as a parameter to predict these violations. These systems thus fail to acknowledge the impact of the change in environment on the behavior of the system and the correlated change in predictive patterns; hence the prediction itself.
Thus, in the light of the above-mentioned challenges, it is evident that, there is a need for a system and method to predict systematically the state of the information technology (IT) system components based on current and past system states and system component states, overall systemic behavior, known system behavioral rules and load conditions/usage characteristics.